The author of this invention has worked in the field of machine vision inspection for over 15 years. There have been many advancements in the field of machine vision during the past ten years, in particular the speed at which the image processing algorithms can process the information. There have also been advances in the resolution of the sensor (CCD and CMOS) used to acquire the images of objects under inspection. Industry, in particular the aerospace and automotive industries have long desired a robust method for the detection of surface porosity. Small pores or holes that appear on the surface of a machined metal component divulge the evidence of porosity.
There are three types of porosity produced from different root causes. The term “Gas Porosity” refers to hydrogen gas within a casting. Molten aluminum has such an affinity for hydrogen that it will disassociate it from other molecules, such as water and form a solution with it. As with most solutions, as the temperature drops the hydrogen becomes less soluble and precipitates as hydrogen gas. The greater the amount of hydrogen in the molten aluminum and the slower it solidifies the greater the hydrogen voids will be. These voids are generally smooth, round or slightly elongated and may be somewhat localized to the areas of the casting that solidify last. This type of porosity is generally undetectable visually since the surface of the casting solidifies quickest preventing the hydrogen from forming holes large enough to be visible on the surface except by using fluorescent penetrant inspection. However, after the removal of material from the surface of the casting by a machining operation the porosity generally becomes visible.
The term “Gas Holes” refers to generally large and more localized voids than gas porosity but they retain the smooth, round or slightly elongated shape. They are usually caused by reaction in the mold media producing gas that will bubble through the molten metal. This type of porosity is generally appears as larger voids on the surface of castings after machining operations.
The term “Shrinkage Porosity” refers to a type of porosity that has a rough irregular shape. It is caused by a lack of adequate feed metal during solidification. This type of porosity is extreme and is observed as variations in the casting shape or voids in the surface.
The detection methods used for porosity depend on the type of porosity. If the only concern is porosity exposed on the surface of a casting then limits can be set for the maximum allowable size. Visual inspection standards are assigned and human inspection is the preferred technique. However, if internal porosity is the major concern, then radiography (x-ray inspection) is the most common detection method. There are other inspection technologies that can be used for the detection of internal flaws they include Eddy Current Inspection, Fluorescent (Dye) Penetrant Inspection, and scanning electron microscopy (SEM) Imaging. All of before mentioned techniques are usually applied in an area removed from the production environment, these are referred to as Off-Line inspection techniques.
The majority of “In-Process” or “On-Line” inspections for porosity related defects are normally performed by qualified human inspectors. The inspection tasks can be very difficult because of complex inspection specifications written to handle the wide variation of porosity characteristics. The size of the porosity is the primary characteristic. Any occurrence of porosity larger than a specified diameter (or dimension) constitutes a defective condition. The occurrence of two or more smaller defects within a specified proximity to each other also constitutes a defective condition. If more than a specified number of porosity defects appear on the entire component this constitutes as defective condition. If the density of smaller dimension porosity that are not considered a defective condition individually but are present as a cluster (small proximity as specified in the specification) then this condition will constitute a defective condition. The number of conditions constituting a defective condition can be considered an overwhelming task of the human inspector and often results in acceptable product being rejected as “bad” or defective product being accepted as “good”.
The specification of porosity limits for commercial castings may use MIL-STD-2175, ASTM B26, Aluminum Association's AA-CS-M Series, ASTM E155 (Radiometric) or the inspection or engineering specifications of the individual customer. Most engineering porosity inspection specifications have been written for human inspection. There is currently a realization by engineering and inspection departments that the specifications must be revised to take advantage of the developing automated machine vision inspection technology described in this present invention.
The configuration and position of the essential components with respect to each other is very important to the functionality of the present invention. The location of the illumination system with respect to the image sensor is critical, and will determine the type and size of imperfections that can be detected. The resolution of the image sensor is an important factor in ability of the invention to reliably isolated imperfections (such as porosity) from background information. The capability of the present invention improves with the use of larger the image sensors and the number of picture elements (Pixels) implemented in the sensors. The current technology implements image sensors that range in size from 640 (Horizontal)×480 (Vertical) to 4000 (Horizontal)×4000 (Vertical) pixels. The resolving capability of the invention will improve as a function of increasing the number of available pixels in the acquisition device. When viewing objects that are rectangular in shape the present invention implements a sensor with format that closely mirrors the shape of the object, such as a sensor with a pixel resolution of 3,500 (H)×2,600 (V).
The defect detection can be estimated using the simple formula
      Feature    ⁢                  ⁢    Resolution    ⁢                  ⁢          (              mm        /        pixel            )        =                                                                        Observed                ⁢                                                                  ⁢                field                ⁢                                                                  ⁢                of                ⁢                                                                  ⁢                view                            ⁢                                                                                                                      (                              FOV                ⁢                                                                  ⁢                in                ⁢                                                                  ⁢                mm                            )                                                  Sensor        ⁢                                  ⁢        Resolution        ⁢                                  ⁢                  (                      #            ⁢                                                  ⁢            pixels                    )                      ⁢                  ⁢                  =          Length      ⁢                          ⁢              Dimension        /                  pixel          .                    Where smaller Feature Resolution values provide better detection of imperfections.
The typical implementation technique requires that imperfections must be larger than a single pixel. Image noise and background variations often generate information that is a single pixel in size and should be eliminated with filtering techniques used in the image processing system. A typical inspection can isolate imperfections with a diameter of 400 μm in a 500 mm FOV when using an 8-megapixel sensor. Higher resolution sensors can isolate even smaller imperfections. Another important factor in proper selection of sensor technology is the grayscale resolution, or depth of the image. The depth of the image is referenced in the number bits, the greater the number of bits the greater the signal to noise ratio of the image. A sensor with 8-bit grayscale resolution is capable of discerning 256 levels of gray information, 28=256. An image sensor with 14-bit grayscale resolution is capable of identifying 16,384 unique levels of grayscale information. This is extremely important if you consider that the lowest two or three bits of the information as being subject to noise. In an 8-bit image, the lowest three bits correlates into 3.125% of the sensor's range. The lowest three bits of a 14-bit image is approximately 0.049% of the sensor's range. As future improvements of image sensor technology materialize the spatial resolution capabilities of the present invention will also improve.
The present invention provides a method and device that can perform the visual inspection of machined metal components for surface porosity defectives. The invention offers the unique capability of isolating surface porosity on all surfaces that are visible to an electronic imaging system and accurately measuring the size (dimensions) of the porosity defects. The individual measurements can then be applied to the one or more inspection criteria as set forth in an inspection specification. Furthermore, the present invention provides 100% inspection capability so that it can be applied on-line in the production environment maintaining a specified quality level of out-going product.